VLA-Arena: Benchmarking Vision-Language-Action Models
Analysis
This paper introduces VLA-Arena, a comprehensive benchmark designed to evaluate Vision-Language-Action (VLA) models. It addresses the need for a systematic way to understand the limitations and failure modes of these models, which are crucial for advancing generalist robot policies. The structured task design framework, with its orthogonal axes of difficulty (Task Structure, Language Command, and Visual Observation), allows for fine-grained analysis of model capabilities. The paper's contribution lies in providing a tool for researchers to identify weaknesses in current VLA models, particularly in areas like generalization, robustness, and long-horizon task performance. The open-source nature of the framework promotes reproducibility and facilitates further research.
Key Takeaways
- •Introduces VLA-Arena, a new benchmark for Vision-Language-Action models.
- •Uses a structured task design framework with orthogonal axes for difficulty.
- •Identifies limitations in current VLA models, such as poor generalization and robustness.
- •Provides an open-source framework to promote reproducibility and further research.
“The paper reveals critical limitations of state-of-the-art VLAs, including a strong tendency toward memorization over generalization, asymmetric robustness, a lack of consideration for safety constraints, and an inability to compose learned skills for long-horizon tasks.”